Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	36,508
2	Mississippi	33,764
3	Florida	33,357
4	Alabama	32,462
5	North Dakota	30,906
6	Arizona	30,329
7	South Carolina	29,440
8	Iowa	29,395
9	Tennessee	29,031
10	Georgia	28,893
11	Arkansas	28,833
12	Texas	27,646
13	South Dakota	27,601
14	Nevada	26,665
15	Nebraska	24,714
16	Idaho	24,601
17	Utah	24,210
18	New York	24,124
19	Illinois	24,086
20	Wisconsin	24,034
21	Rhode Island	23,670
22	New Jersey	23,641
23	Oklahoma	23,077
24	Missouri	22,108
25	District of Columbia	21,989
26	Delaware	21,815
27	Kansas	21,424
28	Maryland	21,136
29	California	21,123
30	North Carolina	20,751
31	Massachusetts	19,578
32	Indiana	18,938
33	Minnesota	18,419
34	Virginia	17,792
35	Kentucky	17,030
36	Connecticut	16,351
37	Puerto Rico	15,867
38	New Mexico	14,534
39	Michigan	14,147
40	Montana	13,789
41	Ohio	13,594
42	Pennsylvania	13,135
43	Colorado	12,743
44	Alaska	12,712
45	Washington	12,282
46	Wyoming	11,237
47	West Virginia	9,278
48	Hawaii	9,151
49	Oregon	8,243
50	New Hampshire	6,357
51	Maine	4,105
52	Vermont	2,860

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	582
2	South Dakota	483
3	Wisconsin	444
4	Utah	365
5	Montana	346
6	Nebraska	279
7	Oklahoma	248
8	Arkansas	242
9	Wyoming	242
10	Iowa	238
11	Alabama	232
12	Minnesota	214
13	Missouri	212
14	Alaska	210
15	South Carolina	210
16	Indiana	200
17	Idaho	195
18	Kentucky	184
19	Mississippi	179
20	Tennessee	178
21	Nevada	176
22	Puerto Rico	167
23	Illinois	160
24	Delaware	156
25	Texas	155
26	Kansas	154
27	North Carolina	140
28	New Mexico	129
29	Louisiana	122
30	Virginia	122
31	Georgia	118
32	Florida	113
33	West Virginia	112
34	Ohio	102
35	Massachusetts	100
36	Maryland	98
37	Colorado	96
38	California	87
39	New Jersey	86
40	New Hampshire	80
41	Pennsylvania	80
42	New York	78
43	District of Columbia	76
44	Michigan	74
45	Oregon	71
46	Arizona	70
47	Hawaii	69
48	Washington	59
49	Connecticut	51
50	Rhode Island	50
51	Maine	21
52	Vermont	16

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,816
2	New York	1,686
3	Massachusetts	1,379
4	Connecticut	1,265
5	Louisiana	1,199
6	Rhode Island	1,055
7	Mississippi	1,012
8	District of Columbia	894
9	Arizona	783
10	Illinois	714
11	Michigan	713
12	Florida	683
13	South Carolina	670
14	Delaware	662
15	Georgia	659
16	Maryland	654
17	Pennsylvania	647
18	Texas	568
19	Indiana	545
20	Nevada	526
21	Alabama	521
22	Arkansas	472
23	Iowa	439
24	New Mexico	425
25	Ohio	421
26	California	408
27	Virginia	383
28	Minnesota	378
29	Tennessee	373
30	North Dakota	363
31	Missouri	361
32	Colorado	360
33	North Carolina	348
34	New Hampshire	325
35	Washington	293
36	South Dakota	280
37	Kentucky	277
38	Idaho	269
39	Oklahoma	265
40	Nebraska	260
41	Kansas	239
42	Wisconsin	238
43	Puerto Rico	214
44	West Virginia	199
45	Montana	174
46	Utah	149
47	Oregon	135
48	Hawaii	109
49	Maine	105
50	Vermont	92
51	Wyoming	91
52	Alaska	73

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	7
2	Arkansas	4
3	Hawaii	4
4	Louisiana	4
5	South Dakota	4
6	Delaware	3
7	Florida	3
8	Georgia	3
9	Mississippi	3
10	Ohio	3
11	South Carolina	3
12	Tennessee	3
13	Illinois	2
14	Iowa	2
15	Kansas	2
16	Nevada	2
17	Puerto Rico	2
18	Texas	2
19	Arizona	1
20	California	1
21	District of Columbia	1
22	Idaho	1
23	Indiana	1
24	Kentucky	1
25	Massachusetts	1
26	Minnesota	1
27	Missouri	1
28	Montana	1
29	Nebraska	1
30	New Mexico	1
31	North Carolina	1
32	Oklahoma	1
33	Pennsylvania	1
34	Utah	1
35	Virginia	1
36	Wisconsin	1
37	Alabama	0
38	Alaska	0
39	Colorado	0
40	Connecticut	0
41	Maine	0
42	Maryland	0
43	Michigan	0
44	New Hampshire	0
45	New Jersey	0
46	New York	0
47	Oregon	0
48	Rhode Island	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	160,319	1	99
Chattahoochee	Georgia	154,305	2	99
Trousdale	Tennessee	149,504	3	99
Lafayette	Florida	146,402	4	99
Lake	Tennessee	133,267	5	99
Davidson	Tennessee	42,102	207	93
Richland	South Carolina	37,808	286	90
York	South Carolina	20,041	1218	61
Orange	California	17,705	1438	54
Pierce	Washington	10,172	2286	27

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,085	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	599	719	77
Davidson	Tennessee	455	1013	67
Orange	California	405	1126	64
York	South Carolina	299	1418	54
Pierce	Washington	253	1566	50

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.